Automatic Classification of Infant Sleeping Postures Using an Infrared Camera

Abstract: The sleeping posture is crucial determinant of infant growth and development. Sleeping in the prone position is associated with a higher risk of sudden infant death syndrome. Therefore, medical recommendations advocate placing infants in the supine position during sleep. Furthermore, certain medical conditions, such as cranial deformity, hip dislocation, and torticollis, may manifest as head-turn preferences, wherein infants consistently face a specific direction, either right or left. Detecting and addressing these sleeping postures are critical for preventing accidental infant deaths during sleep and identifying potential underlying health issues. In this study, we present an automatic method for classifying infant sleeping postures into four categories: supine, prone, right lateral, and left lateral, using only videos. Although various methods exist for classifying sleeping postures during infancy, such as those involving acceleration and pressure seat sensors, they often require physical attachments that may cause discomfort to the infants. To address this limitation, we present a contactless approach that employs video images recorded using an infrared camera. The camera was positioned to record the entire infant bedding area without imposing restrictions on the installation angle. We analyzed the video data collected from the home of each participant and classified the sleeping postures of the participants into four categories. Subsequently, the classification accuracy was calculated for each night. The participants of the experiment were two infants under one year of age. To evaluate data accuracy, we excluded instances of data involving individuals other than the participants and data outside the field of view of the camera. “Vision Pose,” a skeleton estimation software capable of detecting joint points in images, was employed for body position analysis. Specifically, we extracted the two-dimensional coordinates of eight joint points: both shoulders, both elbows, both hips, shoulder center, and hip center. We classified the infant sleeping postures by measuring the distance between these joint points. A linear support vector machine was applied to the features, and classification was conducted in two steps. In the initial step, the sleep data were categorized into two groups: supine or prone and right lateral or left lateral. Subsequently, each of these categories was further divided into two classifications, yielding four types of sleeping postures. Our proposed model demonstrates an impressive average accuracy of 92.3% in estimating the four sleeping postures: supine, prone, right lateral, and left lateral. Our study establishes the feasibility of non-contact sleeping posture classification using an infrared camera. This approach holds promising potential for real-life home environments and childcare facilities, where continuous monitoring of infant sleeping postures can significantly contribute to promoting safe sleep practices and early identification of potential health concerns.

Keywords: Sleeping Posture, Skeleton Detection, Infants, Posture Classification

DOI: 10.54941/ahfe1004352

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